Push down filters are nothing new, but with KSQL we can now start to push down our filters for streaming analysis. As the old saying goes, the fastest data to process is data you don’t have to load, and this is especially true in streaming systems. This talk will look proof-of-concept (e.g. code, but not code you should run in production) data sources built to push down filtering operations into KSQL from other stream processing systems. During this talk you will learn more about how Spark Streaming & Apache BEAM data sources work, and the work required to add filter push down in Spark Structured Streaming. While this talk will do its best to avoid summoning Cthulhu, mixing evaluation engines is a delicate exercise.